What is an AI hallucination? Definition and pitfall to avoid

Hallucination is not a bug, it is a structural property of LLMs. Four mechanisms produce it. Excessive generalisation: the model extrapolates a plausible response from patterns seen in training, even when the precise topic was not covered. Interpolation between contradictory sources: the model merges diverging information into a smooth but inaccurate statement. Pressure to respond: without guardrail, the model prefers to generate a confident response rather than acknowledge ignorance. Temporal mismatch: models trained before a given date are unaware of subsequent developments (knowledge cutoff). Three factors significantly reduce hallucinations: use of inference-time retrieval (RAG or web search), explicit instruction to the model to flag uncertainties, and systematic human validation on outputs with stakes. None eliminates the phenomenon entirely. According to 2025-2026 benchmarks, the best models still hallucinate on 5 to 15% of complex factual queries.